A comparison of two open-source crop simulation models for a potato crop

Quintero, Diego; Díaz, Eliécer

doi:10.15446/agron.colomb.v38n3.82525

Cited by 5 publications

(2 citation statements)

References 9 publications

(12 reference statements)

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“…The AOS parameters are calibrated using biomass data measured in situ. Foster et al (2017) showed that AOS can provide a good fit for yield, and it has been tested in wheat, rice and potato (Quintero & Díaz, 2022).…”

Section: Aos Modelmentioning

confidence: 99%

“…The main advantage of the DIT over other simulation tools is that it includes several crop models and various irrigation strategies, which allow for a comprehensive assessment of crop yields in variable environments. Kassing et al (2020);Quintero and Díaz (2020), Kuschel‐Otárola, Schütze, et al (2020), Kuschel‐Otárola, Rivera, et al (2020), and Orduña et al (2019) applied the AOS and DIT to simulate and optimize problems related to crops.…”

Section: Introductionmentioning

confidence: 99%

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Irrigation optimization in green asparagus (Asparagus officinalis L.) using the AquaCrop model and evolutionary strategy algorithms

Cruz‐Bautista

López‐Cruz

Ochoa‐Meza

et al. 2023

Irrigation and Drainage

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Appropriate irrigation water management has attracted considerable attention because it is crucial to guarantee water and food security worldwide. This study aims to solve a water usage optimization problem for asparagus farming using the Deficit Irrigation Toolbox (DIT) optimization tool and AquaCrop‐OS model. Seven irrigation strategies were planned to assess the yield response of asparagus spears in the region near Caborca, Sonora, Mexico. Three of them were defined according to the usual practices of conventional irrigation, and the other four involved the optimization approach. Crop water productivity responses to 750, 1000, 1250 and 1500 mm of supplied water for the crop growing season were generated and assessed for each strategy. Using the irrigation optimization strategy, which involved a defined decision table using the covariance matrix adaptation evolutionary strategy, resulted in increased water productivity, making it the best solution to the optimization problem. A spear yield of 11.3–12.1 t ha−1 was obtained using an irrigation depth of 1200 mm, thus resulting in a reduced irrigation depth of 580–615 mm (>30%) for the 2019 growing season and 130–165 mm (±10%) for the 2020 growing season. The novelty of the study is that by formulating a dynamic optimization problem using the AquaCrop‐OS model and DIT, it is demonstrated that the water currently used for irrigation of asparagus crops can be reduced by up to 30%.

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Section: Aos Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Irrigation optimization in green asparagus (Asparagus officinalis L.) using the AquaCrop model and evolutionary strategy algorithms

Cruz‐Bautista

López‐Cruz

Ochoa‐Meza

et al. 2023

Irrigation and Drainage

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Digital Yield Predictions

Lipping,

Ranta

2023

Digital Agriculture

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Proposing a combined method for the estimation of spatial and temporal variation of crop water productivity under deficit irrigation scenarios based on the AquaCrop model

2022

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Deficit irrigation is a management strategy to improve crop water productivity, especially in arid and semi-arid regions. Soil characteristics and weather parameters are among the factors affecting crop water productivity in water stress conditions. Due to spatial changes in soil characteristics and temporal and spatial variations in meteorological parameters, it can be expected that crop water productivity will also have temporal and spatial variations. In this study, by combining the Geographic Information System (GIS) with the grid weather generation tools from the Crop Growth Monitoring System (CGMS) and the plug-in version of the AquaCrop, a combined method was developed to investigate the spatial and temporal variation of crop yield, seasonal crop evapotranspiration, and water productivity of maize under various irrigation scenarios. The proposed model was implemented in a case study in the west of Iran. The study area was divided into 37 grid weather with 5 * 5 km and 19 soil units. By overlaying soil units and grid weathers, 94 homogeneous units were created. The model was executed for 94 homogeneous areas, using calibrated crop file of grain maize under four irrigation scenarios of 40, 60, 80, and 100% of potential irrigation requirement (S40, S60, S80, and S100, respectively) for 28 years (1988–2015) of weather data (10,528 runs). The results showed that by increasing water stress, the percentage of spatial and temporal variation of the studied parameters (crop yield, seasonal crop water requirement, and water productivity) would be increased. The percentage of spatial changes in crop yield and crop water productivity was more significant than temporal changes. The average of crop water productivity in the scenarios of S100, S80, S60, and S40 was determined as 1.5, 1.4, 1.2, and 0.5 kg m−3, respectively.

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A comparison of two open-source crop simulation models for a potato crop

Cited by 5 publications

References 9 publications

Irrigation optimization in green asparagus (Asparagus officinalis L.) using the AquaCrop model and evolutionary strategy algorithms

Irrigation optimization in green asparagus (Asparagus officinalis L.) using the AquaCrop model and evolutionary strategy algorithms

Digital Yield Predictions

Proposing a combined method for the estimation of spatial and temporal variation of crop water productivity under deficit irrigation scenarios based on the AquaCrop model

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